Contemporary computing devices allow users to enter handwritten words (e.g., in cursive handwriting and/or printed handwritten characters) and symbols (e.g., a character in Far East languages). The words and symbols can be used as is, e.g., to function as readable notes and so forth, or can be converted to text for more conventional computer uses. To convert to text, for example, as a user writes strokes representing words or other symbols onto a touch-sensitive computer screen or the like, a handwriting recognizer (e.g., trained with millions of samples, employing a dictionary, context and other rules) is able to convert the handwriting data into dictionary words or symbols. In this manner, users are able to enter textual data without necessarily needing a keyboard.
Contemporary handwriting recognizers are not one hundred percent accurate in recognizing words. Because of this, one type of recognizer returns a list of alternates, ranked according to probability by the recognizer. Via a user interface, systems may provide a list of these alternates, from which the user can select a different word instead of the word the recognizer initially guessed as the one that the user most likely intended.
However, alternates are provided on a per word basis, and many users do not at first recognize this concept. As a result, when a user is seeking alternates for a given word, the user often becomes confused because the alternates provided upon menu selection often do not appear to correspond to the word for which the user wants to select an alternate. In general, selection of alternates has not heretofore been a straightforward or intuitive operation.